library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(cowplot)
library(colorblindr)
library(viridis)
library(progeny)
library(destiny)

coi <- params$cell_type_super
cell_sort <- params$cell_sort
cell_type_major <- params$cell_type_major
louvain_resolution <- params$louvain_resolution
louvain_cluster <- params$louvain_cluster

1 Cluster markers

1.1 Major Myeloid.super markers for cell assign

### load all data ---------------------------------
source("_src/global_vars.R")

# seu_obj <- read_rds(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_processed.rds"))
seu_obj <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/outs_pre/", coi, "_seurat_", louvain_resolution, ".rds"))
# seu_obj <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_highqc.rds"))

myfeatures <- c("umapharmony_1", "umapharmony_2", "sample", louvain_cluster, "doublet", "nCount_RNA", "nFeature_RNA", "percent.mt", "doublet_score")

plot_data_wrapper <- function(cluster_res) {
  cluster_res <- enquo(cluster_res)
  as_tibble(FetchData(seu_obj, myfeatures)) %>% 
    left_join(meta_tbl, by = "sample") %>% 
    rename(cluster = !!cluster_res) %>% 
    mutate(cluster = as.character(cluster),
           tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite)))
}

plot_data <- plot_data_wrapper(louvain_cluster)

1.2 Subtype currated markers

helper_f <- function(x) ifelse(is.na(x), "", x)

markers_v7_super[[coi]] %>% 
  group_by(subtype) %>% 
  mutate(rank = row_number(gene)) %>% 
  spread(subtype, gene) %>% 
  mutate_all(.funs = helper_f) %>% 
  formattable::formattable()

rank	cDC1	cDC2	Cycling.M	dissociated	M1.CDKN1C	M1.CXCL10	M1.S100A8	M1.S100A9	M2.MARCO	M2.SELENOP	Mast.cell	mDC	Neutrophil	pDC
1	BATF3	CD1C	CDC20	BTG1	CD52	CCL8	APOBEC3A	AREG	CCL20	A2M	CPA3	BIRC3	CSF3R	BCL11A
2	BTLA	CLEC10A	CDK1	DNAJB1	CDKN1C	CXCL10	FCN1	EREG	CXCL3	CCL3L1	CTSG	CCL19	CXCR2	CLIC3
3	CLEC9A	FCER1A	MKI67	DUSP1	LST1	CXCL11	S100A8	S100A8	FN1	CCL4L2	GATA2	CCR7	FCGR3B	GZMB
4	IRF8	HLA-DQA1	PTTG1	EGR1	SMIM25	IFIT1	S100A9	S100A9	HAMP	FCGBP	HPGD	IDO1	IL1R2	JCHAIN
5	XCR1	S100B	TOP2A	FOS	TCF7L2	TNFSF10	VCAN	THBS1	LYVE1	SELENOP	HPGDS	MARCKSL1	NAMPT	PTGDS
6				FOSB					MARCO		KIT		TXNIP
7				HSPA1A					PLIN2		MS4A2
8				HSPA1B					TGM2		TPSAB1
9				HSPA6							TPSB2
10				JUN
11				JUNB
12				KLF2
13				MT1E
14				MT1G
15				MT1H
16				MT1X

1.3 Subtype cluster markers

# marker_tbl <- read_tsv(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_markers.tsv")) %>% 
#   filter(resolution == louvain_resolution)
marker_tbl <- read_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/outs_pre/", coi, "_markers_", louvain_resolution, ".tsv"))
# marker_tbl <- read_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_highqc_markers_02.tsv"))

## Hypergeometric test --------------------------------------

test_set <- marker_tbl %>% 
  group_by(cluster) %>% 
  filter(!str_detect(gene, "^RPS|^RPL")) %>% 
  slice(1:50) %>% 
  mutate(k = length(cluster)) %>% 
  ungroup %>%
  select(cluster, gene, k) %>% 
  mutate(join_helper = 1) %>% 
  group_by(cluster, join_helper, k) %>%
  nest(test_set = gene)

markers_doub_tbl <- markers_v7 %>% 
  enframe("subtype", "gene") %>% 
  filter(!(subtype %in% unique(c(coi, cell_type_major)))) %>% 
  unnest(gene) %>% 
  group_by(gene) %>% 
  filter(length(gene) == 1) %>% 
  mutate(subtype = paste0("doublet.", subtype)) %>% 
  bind_rows(tibble(subtype = "Mito.high", gene = grep("^MT-", rownames(seu_obj), value = T)))

ref_set <- markers_v7_super[[coi]] %>% 
  bind_rows(markers_doub_tbl) %>% 
  group_by(subtype) %>% 
  mutate(m = length(gene),
         n = length(rownames(seu_obj))-m,
         join_helper = 1) %>% 
  group_by(subtype, m, n, join_helper) %>%
  nest(ref_set = gene)

hyper_tbl <- test_set %>% 
  left_join(ref_set, by = "join_helper") %>% 
  group_by(cluster, subtype, m, n, k) %>%
  do(q = length(intersect(unlist(.$ref_set), unlist(.$test_set)))) %>%
  mutate(pval = 1-phyper(q = q, m = m, n = n, k = k)) %>%
  ungroup %>%
  mutate(qval = p.adjust(pval, "BH"),
         sig = qval < 0.01)

# hyper_tbl %>% 
#   group_by(subtype) %>% 
#   filter(any(qval < 0.01)) %>%
#   ggplot(aes(subtype, -log10(qval), fill = sig)) +
#   geom_bar(stat = "identity") +
#   facet_wrap(~cluster) +
#   coord_flip()
  
low_rank <- str_detect(unique(hyper_tbl$subtype), "doublet|dissociated")
subtype_lvl <- c(sort(unique(hyper_tbl$subtype)[!low_rank]), sort(unique(hyper_tbl$subtype)[low_rank]))
  
cluster_label_tbl <- hyper_tbl %>% 
  mutate(subtype = ordered(subtype, levels = subtype_lvl)) %>% 
  arrange(qval, subtype) %>%
  group_by(cluster) %>% 
  slice(1) %>% 
  mutate(subtype = ifelse(sig, as.character(subtype), paste0("unknown_", cluster))) %>% 
  select(cluster, cluster_label = subtype) %>% 
  ungroup %>% 
  mutate(cluster_label = make.unique(cluster_label, sep = "_"))

seu_obj$cluster_label <- unname(deframe(cluster_label_tbl)[as.character(unlist(seu_obj[[paste0("RNA_snn_res.", louvain_resolution)]]))])
plot_data$cluster_label <- seu_obj$cluster_label

cluster_n_tbl <- seu_obj$cluster_label %>% 
  table() %>% 
  enframe("cluster_label", "cluster_n") %>% 
  mutate(cluster_nrel = cluster_n/sum(cluster_n))

marker_sheet <- marker_tbl %>% 
  left_join(cluster_label_tbl, by = "cluster") %>% 
  group_by(cluster_label) %>% 
  filter(!str_detect(gene, "^RPS|^RPL")) %>% 
  slice(1:50) %>% 
  mutate(rank = row_number(-avg_logFC)) %>% 
  select(cluster_label, gene, rank) %>% 
  ungroup %>% 
  mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>% 
  spread(cluster_label, gene) %>% 
  mutate_all(.funs = helper_f)

marker_tbl_annotated <- marker_tbl %>% 
  left_join(cluster_label_tbl, by = "cluster") %>% 
  left_join(cluster_n_tbl, by = "cluster_label") %>% 
  select(-cluster, -resolution) %>% 
  mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>% 
  arrange(cluster_label, -avg_logFC, p_val_adj)

write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_marker_sheet.tsv"))

write_tsv(marker_tbl_annotated, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_marker_table_annotated.tsv"))

formattable::formattable(marker_sheet)

rank	M1.S100A8	M1.CXCL10	M2.SELENOP	M2.MARCO	cDC1	cDC2	mDC	pDC	Mast.cell	Neutrophil	Cycling.M	dissociated	doublet.Fibroblast	doublet.Fibroblast_1	doublet.Ovarian.cancer.cell	doublet.T.cell
1	S100A9	CXCL10	SELENOP	MT1G	CPVL	FCER1A	BIRC3	GZMB	TPSAB1	PLCG2	STMN1	HSPA6	COL1A1	MMP9	SLPI	IL32
2	S100A8	CXCL11	SLC40A1	MARCO	CLEC9A	S100B	CCL19	PTGDS	TPSB2	C3	H2AFZ	HSPA1A	COL1A2	CTSK	WFDC2	CCL5
3	VCAN	CCL8	CCL3L1	MT1X	LGALS2	CLEC10A	CCR7	JCHAIN	CPA3	MTRNR2L12	MKI67	DNAJB1	COL3A1	ACP5	KRT19	CD2
4	FCN1	IFIT2	CCL4L2	MT1H	CPNE3	CD1C	MARCKSL1	CLIC3	HPGD	MTRNR2L8	CENPF	HSPH1	CALD1	SLC9B2	LCN2	GZMA
5	S100A12	IFIT1	CCL3	PLIN2	IDO1	AREG	TXN	BCL11A	MS4A2	IGKC	HIST1H4C	HSPA1B	SPARC	ANPEP	KRT18	CD3D
6	THBS1	TNFSF10	DAB2	CXCL3	CST3	HLA-DPB1	IDO1	ITM2C	CTSG	CSF3R	TOP2A	HSPB1	SELENOP	SPARC	CLU	NKG7
7	LYZ	ISG15	RNASE1	CCL20	C1orf54	HLA-DQA1	LAMP3	LILRA4	CD69		TUBB	HSP90AA1	TAGLN	ANKH	KRT8	TRAC
8	CD55	RSAD2	A2M	FN1	SNX3	PPA1	IL7R	IRF7	CLU		TUBA1B	HSPD1	C1QB	LMNA	KRT7	GNLY
9	RETN	IFIT3	SLCO2B1	TIMP1	WDFY4	HLA-DQB1	PPA1	PLAC8	HPGDS		TYMS	BAG3	C1QA	CD109	CD24	LTB
10	CD52	GBP1	F13A1	MT2A	DNASE1L3	HLA-DPA1	CCL22	TCF4	RHEX		HMGN2	ZFAND2A	C1QC	SIGLEC15	S100A1	TRBC2
11	EREG	CXCL9	FCGBP	CXCL8	IRF8	JAML	CCL17	TCL1A	KIT		HMGB2	G0S2	FOLR2	TNFRSF11A	S100A13	IL7R
12	AC020656.1	MX1	CCL4	MT1E	S100B	CD1E	EBI3	IGKC	SLC18A2		HMGB1	PLIN2	NNMT	ANXA2	IGFBP2	TRBC1
13	CD300E	APOBEC3A	LGMN	CXCL2	RGCC	HLA-DRA	FSCN1	IRF8	VWA5A		PCLAF	IER5	DCN	VIM	C19orf33	CD3G
14	APOBEC3A	CCL2	APOE	NUPR1	CLNK	HLA-DRB1	NUB1	IL3RA	GATA2		NUSAP1	SERPINH1	F13A1	ATP6V0D2	MDK	CD3E
15	AREG	ISG20	HTRA1	HAMP	XCR1	HLA-DQA2	DUSP5	CCDC50	AREG		PTTG1	ADM	SLC40A1	CD99	CLDN3	GZMK
16	CFP	GBP4	C1QB	EREG	HLA-DPB1	PKIB	CSF2RA	TSPAN13	IL1RL1		CKS1B	HSPE1	ACTA2	SUCNR1	EPCAM	SYNE2
17	SMIM25	HERC5	C1QC	CTSL	CCND1	LGALS2	CST7	UGCG	LTC4S		ASPM	DNAJA4	GPR34	MATK	MMP7	ETS1
18	MCEMP1	GBP5	GPR34	LYVE1	RAB7B	HLA-DRB5	BASP1	PPP1R14B	HDC		PCNA	SLC2A3	SLCO2B1	MMP14	MAL2	CXCL13
19	S100A4	IFIH1	FOLR2	CFD	NAAA	CSF2RA	DAPP1	PLD4	LMO4		TK1	CRYAB	TMEM176B	HCFC1R1	MUC1	CD7
20	RIPOR2	NT5C3A	C1QA	CYP27A1	HLA-DPA1	HLA-DQB2	CD83	C12orf75	RGS13		SMC4	HILPDA	APOE	MYO1B	ELF3	KLRB1
21	CD36	IL4I1	STAB1	ERO1A	BATF3	HERPUD1	GPR157	MZB1	ADCYAP1		TPX2	CACYBP	A2M	DPP4	LAPTM4B	SPOCK2
22	S100A6	MX2	MRC1	ADM	HLA-DQB1	CST3	HLA-DQA1	TPM2	AL157895.1		CDK1	HK2	IGFBP2	COL6A1	S100A16	CLEC2D
23	CYP1B1	LY6E	MAF	G0S2	HLA-DQA1	PAK1	PTPN1	DERL3	IER2		DUT	CSTB	COL6A2	ITGA2	CRABP2	CST7
24	SERPINA1	IFI6	MS4A7	HK2	TXN	CIITA	CFLAR	LTB	SOCS1		TUBA1C	UBB	MRC1	COL6A2	BCAM	TNFRSF18
25	STXBP2	SAMD9L	C3	SLC2A5	SLAMF7	LTB	LGALS2	SPIB	ANXA1		BIRC5	HSP90AB1	DAB2	NFATC1	TM4SF1	IKZF3
26	LST1	IL1RN	APOC1	CSTB	PPA1	PLAC8	DUSP4	CLEC4C	PPP1R15A		KPNA2	DDIT4	MS4A4A	AK5	S100A14	CD69
27	LILRA5	OAS1	TREM2	PLTP	TACSTD2	CST7	HLA-DPB1	SMPD3	BACE2		NUCKS1	TENT5A	TPM2	FNBP1L	SNCG	ITM2A
28	SLC25A37	PARP14	EGR1	ANGPTL4	SHTN1	CD1A	RFTN1	SERPINF1	RAC2		RAN	FKBP4	LUM	PPIC	CP	LCK
29	FPR1	EPSTI1	PDK4	P4HA1	ASB2	NDRG2	SLCO5A1	MAP1A	LIF		UBE2C	UBC	CTSC	MYO1D	RBP1	DUSP4
30	MNDA	STAT1	NRP1	GPNMB	RAB32	GRASP	EEF1A1	IRF4	GRAP2		SMC2	DNAJB4	MS4A7	EMP1	DSP	TNFRSF4
31	LILRB2	DDX58	PLD3	FTH1	KCNK6	CD74	POGLUT1	SCT	MAOB		CDKN3	BNIP3	COL6A1	CAVIN3	S100A2	ACAP1
32	TIMP1	IFITM3	MSR1	NDRG1	CST7	FILIP1L	LAD1	CYB561A3	CSF1		RANBP1	LDHA	HLA-DRB1	AC092484.1	PERP	GZMH
33	FGR	OASL	EGR2	GAPDH	CADM1	MS4A6A	TRAF1	SOX4	RHOH		RRM2	SLC2A1	CD74	COL27A1	MAL	CD8A
34	NAMPT	USP18	GYPC	TNFRSF1B	BASP1	LYZ	BCL2L14	SELL	KRT1		PARP1	NDRG1	IGF1	RAI14	SMIM22	CD27
35	COTL1	IFI44L	IFI27	GLUL	LSP1	IFITM2	AOC1	APP	LAPTM4A		TUBB4B	UPP1	RARRES2	PPFIBP1	MUC16	TIGIT
36	IRAK3	WARS	AKR1B1	SLC2A3	PTPRE	EEF1A1	CLIC2	SPCS1	SLC45A3		DEK	SLC5A3	MS4A6A	PHETA1	CLDN4	CTSW
37	LYST	IFI35	IGF1	MT1M	GSTP1	HLA-DMA	SPIB	CCDC186	LAT		NASP	C15orf48	ITM2B	DNM1	MSLN	CD52
38	SH3BGRL3	RNF213	CPM	BNIP3	CD74	CKLF	IL2RG	LAMP5	STX3		CLSPN	ERO1A	HLA-DRA	F2R	ASS1	KLRD1
39	NCF2	LAP3	CTSD	EMP3	FGL2	ALDH2	AL031316.1	IGHM	UBB		HMMR	DNAJA1	HLA-DPB1	DIXDC1	CDKN2A	CTLA4
40	CCR2	SAMD9	FCGR2A	VCAN	FNBP1	EEF1B2	CD1E	LINC00996	LAX1		H2AFV	RGCC	HLA-DPA1	AKAP6	FOLR1	EVL
41	FLNA	VAMP5	SLC1A3	LDHA	HLA-DRB1	GAPT	CD274	SEL1L3	SMYD3		CCNB1	BHLHE40	MYL9	NRIP3	PDZK1IP1	CD247
42	MXD1	XAF1	PLAU	BNIP3L	TAP1	CD1D	PTGIR	MAPKAPK2	CNRIP1		CEP55	SERPINE1	CCDC80	RUFY4	SCGB2A1	CCND2
43	LTA4H	TNFSF13B	PLTP	SOD2	ACTG1	STK17B	MREG	SEC61B	MLPH		ANP32B	PPP1R15A	POSTN	C1orf21	MYL9	RORA
44	RGS2	TAP1	MS4A4A	CTSB	EEF1B2	PLD4	SLC7A11	CXCR3	ITM2A		SPP1	CHORDC1	COL6A3	LTBP2	KRT17	IL2RB
45	TUBA1A	C15orf48	AP1B1	FAM162A	HLA-DRA	IGSF6	TVP23A	GPR183	RAB27B		TMPO	IL1RN	CTHRC1	ZNF462	CRYAB	GZMM
46	H3F3A	OAS3	GAL3ST4	FTL	NAP1L1	IFI30	RHOF	OFD1	AL662860.1		LDHB	VEGFA	CD81	HTRA3	CRIP2	APOBEC3G
47	CEBPB	DYNLT1	JUN	VIM	SUB1	SPINT2	LIMCH1	SLC15A4	CAVIN2		DTYMK	TCEAL9	BGN	STEAP1	SLC34A2	CD96
48	CSF3R	OAS2	SGK1	SLC2A1	DUSP4	BASP1	TIFAB	PLP2	ITM2C		HTRA1	TNFSF14	CXCL12	F5	CTTN	RASGRP1
49	CSTA	CCL7	ITM2B	APLP2	ICAM3	GPAT3	IRF4	TXN	CALB2		ARL6IP1	HIF1A-AS2	THY1	LAT	ARHGAP29	CCL4
50	LCP1	CMPK2	CD81	FBP1	TMSB4X	GPR183	SLC41A2	SELENOS	TIMP3		ATAD2	BCL2A1	NBL1	CTTN	SOX17	STK17A

2 Clusters

2.1 sizes

enframe(sort(table(seu_obj$cluster_label))) %>% 
  mutate(name = ordered(name, levels = rev(name))) %>% 
  ggplot() +
  geom_bar(aes(name, value), stat = "identity") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(y = c("#cells"), x = "cluster")

2.2 UMAP

alpha_lvl <- ifelse(nrow(plot_data) < 20000, 0.2, 0.1)
pt_size <- ifelse(nrow(plot_data) < 20000, 0.2, 0.05)

common_layers_disc <- list(  
  geom_point(size = pt_size, alpha = alpha_lvl),
  NoAxes(),
  guides(color = guide_legend(override.aes = list(size = 2, alpha = 1))),
  labs(color = "")
)

common_layers_cont <- list(  
  geom_point(size = pt_size, alpha = alpha_lvl),
  NoAxes(),
  scale_color_gradientn(colors = viridis(9)),
  guides(color = guide_colorbar())
)

ggplot(plot_data, aes(umapharmony_1, umapharmony_2, color = cluster_label)) + 
  common_layers_disc +
  #facet_wrap(~therapy) +
  ggtitle("Sub cluster")

3 Filtering out doublet and dissociated clusters

3.1 UMAP

my_subtypes <- names(clrs$cluster_label[[coi]])
my_subtypes <- c(my_subtypes, unlist(lapply(paste0("_", 1:3), function(x) paste0(my_subtypes, x)))) %>% .[!str_detect(., "doublet|dissociated")]
my_subtypes <- my_subtypes[my_subtypes %in% unique(seu_obj$cluster_label)]
my_subtypes <- my_subtypes[my_subtypes %in% names(clrs$cluster_label[[coi]])]

cells_to_keep <- colnames(seu_obj)[seu_obj$cluster_label %in% my_subtypes]
seu_obj_sub <- subset(seu_obj, cells = cells_to_keep)
seu_obj_sub <- RunUMAP(seu_obj_sub, dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")
seu_obj_sub$cluster_label <- seu_obj$cluster_label[colnames(seu_obj) %in% colnames(seu_obj_sub)]
write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_processed_filtered.rds"))
seu_obj_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_processed_filtered.rds"))

plot_data_sub <- as_tibble(FetchData(seu_obj_sub, c(myfeatures, "cluster_label"))) %>% 
  left_join(meta_tbl, by = "sample") %>% 
  mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
         tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>% 
  mutate(cell_id = colnames(seu_obj_sub),
         cluster_label = ordered(cluster_label, levels = my_subtypes),
         )
  
if (cell_sort == "CD45+") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
}

if (cell_sort == "CD45-") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
}

ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) + 
  common_layers_disc +
  ggtitle("Sub cluster") +
  #facet_wrap(~cluster_label) +
  scale_color_manual(values = clrs$cluster_label[[coi]])

ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) + 
  common_layers_disc +
  ggtitle("Patient") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$patient_id_short)

ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) + 
  # geom_point(aes(umapharmony_1, umapharmony_2), 
  #            color = "grey90", size = 0.01, 
  #            data = select(plot_data_sub, -tumor_supersite)) +
  common_layers_disc +
  ggtitle("Site") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$tumor_supersite)

write_tsv(select(plot_data_sub, cell_id, everything(), -umapharmony_1, -umapharmony_2, -contains("RNA_")), paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_embedding.tsv"))

3.2 add module scores and pathway scores

signature_modules_cd8 <- read_excel("_data/small/signatures/SPECTRUM v5 sub cluster markers.xlsx", sheet = 2, skip = 1, range = "M2:P100") %>% 
  gather(module, gene) %>% 
  na.omit() %>% 
  group_by(module) %>% 
  do(gene = c(.$gene)) %>% 
  {setNames(.$gene, .$module)} %>% 
  setNames(paste0(names(.), ".module"))

signature_modules_cin <- yaml::read_yaml("_data/small/signatures/cgas_sting.yaml") %>% 
  .[lapply(., length) > 1] %>% 
  setNames(paste0(names(.), ".module"))

## compute expression module scores
for (i in 1:length(signature_modules_cd8)) {
  seu_obj_sub <- AddModuleScore(seu_obj_sub, features = signature_modules_cd8[i], name = names(signature_modules_cd8)[i])
  seu_obj_sub[[names(signature_modules_cd8)[i]]] <- seu_obj_sub[[paste0(names(signature_modules_cd8)[i], "1")]]
  seu_obj_sub[[paste0(names(signature_modules_cd8)[i], "1")]] <- NULL
  print(paste(names(signature_modules_cd8)[i], "DONE"))
}

## [1] "CD8.Cytotoxic.module DONE"
## [1] "CD8.Dysfunctional.module DONE"
## [1] "CD8.Naive.module DONE"
## [1] "CD8.Predysfunctional.module DONE"

for (i in 1:length(signature_modules_cin)) {
  seu_obj_sub <- AddModuleScore(seu_obj_sub, features = signature_modules_cin[i], name = names(signature_modules_cin)[i])
  seu_obj_sub[[names(signature_modules_cin)[i]]] <- seu_obj_sub[[paste0(names(signature_modules_cin)[i], "1")]]
  seu_obj_sub[[paste0(names(signature_modules_cin)[i], "1")]] <- NULL
  print(paste(names(signature_modules_cin)[i], "DONE"))
}

## [1] "CIN.module DONE"
## [1] "CIN.responsive.Noncanonical.NFKB.targets.up.module DONE"
## [1] "CIN.responsive.Noncanonical.NFKB.targets.down.module DONE"
## [1] "Noncanonical.NFKB.regulators.up.module DONE"
## [1] "Noncanonical.NFKB.regulators.down.module DONE"
## [1] "Canonical.NFKB.regulators.module DONE"
## [1] "Interferon.regulators.module DONE"
## [1] "EMT.module DONE"
## [1] "Inflammation.module DONE"
## [1] "Motility.and.migration.module DONE"
## [1] "ISG.module DONE"
## [1] "SASP.module DONE"
## [1] "Myofibroblast.module DONE"
## [1] "Fibroblast.module DONE"
## [1] "Mesenchymal.stem.cell.module DONE"
## [1] "Pericytes.module DONE"
## [1] "Smooth.muscle.cell.module DONE"
## [1] "ER.stress.module DONE"
## [1] "IFNg.signaling.module DONE"

## compute progeny scores
progeny_list <- seu_obj_sub@assays$RNA@data[VariableFeatures(seu_obj_sub),] %>%
  as.matrix %>%
  progeny %>%
  as.data.frame %>%
  as.list

names(progeny_list) <- make.names(paste0(names(progeny_list), ".pathway"))

for (i in 1:length(progeny_list)) {
  seu_obj_sub <- AddMetaData(seu_obj_sub,
                                 metadata = progeny_list[[i]],
                                 col.name = names(progeny_list)[i])
}

write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_processed_filtered_annotated.rds"))
seu_obj_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_processed_filtered_annotated.rds"))

3.3 marker heatmap

marker_top_tbl <- marker_sheet[,-1] %>% 
  slice(1:10) %>% 
  as.list %>% 
  .[!str_detect(names(.), "doublet")] %>% 
  enframe("cluster_label_x", "gene") %>% 
  unnest(gene)

plot_data_markers <- as_tibble(FetchData(seu_obj_sub, c("cluster_label", myfeatures, unique(marker_top_tbl$gene)))) %>% 
  gather(gene, value, -c(1:(length(myfeatures)+1))) %>% 
  left_join(meta_tbl, by = "sample") %>% 
  mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
         tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>% 
  mutate(cluster_label = ordered(cluster_label, levels = my_subtypes)) %>% 
  group_by(cluster_label, gene) %>% 
  summarise(value = mean(value, na.rm = T)) %>% 
  group_by(gene) %>% 
  mutate(value = scales::rescale(value)) %>% 
  left_join(marker_top_tbl, by = "gene") %>% 
  mutate(cluster_label_x = ordered(cluster_label_x, levels = rev(names(clrs$cluster_label[[coi]]))))

ggplot(plot_data_markers) +
  geom_tile(aes(gene, cluster_label, fill = value)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  facet_grid(~cluster_label_x, scales = "free", space = "free") +
  scale_fill_gradientn(colors = viridis(9)) +
  labs(fill = "Scaled\nexpression") +
  theme(aspect.ratio = 1,
        axis.line = element_blank(),
        axis.ticks = element_blank(),
        axis.title = element_blank())

# ggsave(paste0("_fig/002_marker_heatmap_", coi, ".pdf"), width = nrow(marker_top_tbl)/6, height = 5)

3.4 composition

3.4.1 per site

comp_site_tbl <- plot_data_sub %>%
  filter(!is.na(tumor_supersite)) %>% 
  group_by(cluster_label, tumor_supersite) %>%
  tally %>%
  group_by(tumor_supersite) %>%
  mutate(nrel = n/sum(n)*100) %>%
  ungroup

pnrel_site <- ggplot(comp_site_tbl) +
  geom_bar(aes(tumor_supersite, nrel, fill = cluster_label),
           stat = "identity", position = position_stack()) +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(fill = "Cluster", y = "Fraction [%]", x = "") +
  scale_fill_manual(values = clrs$cluster_label[[coi]])

pnabs_site <- ggplot(comp_site_tbl) +
  geom_bar(aes(tumor_supersite, n, fill = cluster_label),
           stat = "identity", position = position_stack()) +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(fill = "Cluster", y = "# cells", x = "") +
  scale_fill_manual(values = clrs$cluster_label[[coi]])

plot_grid(pnabs_site, pnrel_site, ncol = 2, align = "h")

# ggsave(paste0("_fig/02_deep_dive_", coi, "_comp_site.pdf"), width = 8, height = 4)

3.4.2 per sample

comp_tbl_sample_sort <- plot_data_sub %>% 
  group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy, cluster_label) %>% 
  tally %>% 
  group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy) %>% 
  mutate(nrel = n/sum(n)*100,
         nsum = sum(n),
         log10n = log10(n),
         label_supersite = "Site",
         label_mutsig = "Signature",
         label_therapy = "Rx") %>% 
  ungroup %>% 
  arrange(desc(therapy), tumor_supersite) %>% 
  mutate(tumor_subsite_rx = paste0(tumor_subsite, "_", therapy)) %>% 
  mutate(tumor_subsite = ordered(tumor_subsite, levels = unique(tumor_subsite)),
         tumor_subsite_rx = ordered(tumor_subsite_rx, levels = unique(tumor_subsite_rx))) %>% 
  arrange(patient_id) %>% 
  mutate(label_patient_id = ifelse(as.logical(as.numeric(fct_inorder(as.character(patient_id)))%%2), "Patient1", "Patient2"))

sample_id_x_tbl <- plot_data_sub %>% 
  mutate(sort_short_x = cell_sort) %>% 
  distinct(patient_id, sort_short_x, tumor_subsite, therapy, sample) %>% 
  unite(sample_id_x, patient_id, sort_short_x, tumor_subsite, therapy) %>% 
  arrange(sample_id_x)

comp_tbl_sample_sort %>% 
  mutate(sort_short_x = cell_sort) %>% 
  unite(sample_id_x, patient_id, sort_short_x, tumor_subsite_rx) %>% 
  select(sample_id_x, cluster_label, n, nrel, nsum) %>% 
  left_join(sample_id_x_tbl, by = "sample_id_x") %>% 
  write_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_subtype_compositions.tsv"))

ybreaks <- c(500, 1000, 2000, 4000, 6000, 8000, 10000, 15000, 20000)

max_cells_per_sample <- max(comp_tbl_sample_sort$nsum)
ymaxn <- ybreaks[max_cells_per_sample < ybreaks][1]

comp_plot_wrapper <- function(y = "nrel", switch = NULL) {
  if (y == "nrel") ylab <- paste0("Fraction\nof cells [%]")
  if (y == "n") ylab <- paste0("Number\nof cells")
  p <- ggplot(comp_tbl_sample_sort, 
              aes_string("tumor_subsite_rx", y, fill = "cluster_label")) + 
    facet_grid(~patient_id, space = "free", scales = "free", switch = switch) +
    coord_cartesian(clip = "off") + 
    scale_fill_manual(values = clrs$cluster_label[[coi]]) + 
    theme(axis.text.x = element_blank(),
          axis.title.y = element_text(angle = 0, vjust = 0.5, hjust = 0.5, 
                                      margin = margin(0, -0.4, 0, 0, unit = "npc")),
          axis.ticks.x = element_blank(),
          axis.title.x = element_blank(),
          axis.line.x = element_blank(),
          strip.text.y = element_blank(),
          strip.text.x = element_blank(),
          strip.background.y = element_blank(),
          strip.background.x = element_blank(),
          plot.margin = margin(0, 0, 0, 0, "npc")) + 
    labs(x = "",
         y = ylab) +
    guides(fill = FALSE)
  if (y == "nrel") p <- p + 
    geom_bar(stat = "identity") +
    scale_y_continuous(expand = c(0, 0), 
                       breaks = c(0, 50, 100), 
                       labels = c("0", "50", "100"))
  if (y == "n") p <- p + 
    geom_bar(stat = "identity", position = position_stack()) +
    scale_y_continuous(expand = c(0, 0), 
                       breaks = c(0, ymaxn/2, ymaxn),
                       limits = c(0, ymaxn),
                       labels = c("", ymaxn/2, ymaxn)) +
    expand_limits(y = c(0, ymaxn)) +
    theme(panel.grid.major.y = element_line(linetype = 1, color = "grey90", size = 0.5))
  return(p)
} 

common_label_layers <- list(
  geom_tile(color = "white", size = 0.15),
  theme(axis.text.x = element_blank(),
        axis.ticks = element_blank(),
        axis.title.x = element_blank(),
        axis.line.x = element_blank(),
        strip.text = element_blank(),
        strip.background = element_blank(),
        plot.margin = margin(0, 0, 0, 0, "npc")),
  scale_y_discrete(expand = c(0, 0)),
  labs(y = ""),
  guides(fill = FALSE),
  facet_grid(~patient_id, 
             space = "free", scales = "free")
)

comp_label_site <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_supersite, patient_id), 
       aes(tumor_subsite_rx, label_supersite, 
           fill = tumor_supersite)) + 
  scale_fill_manual(values = clrs$tumor_supersite) +
  common_label_layers

comp_label_rx <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_therapy, consensus_signature, patient_id), 
       aes(tumor_subsite_rx, label_therapy, 
           fill = therapy)) + 
  scale_fill_manual(values = c(`post-Rx` = "gold3", `pre-Rx` = "steelblue")) +
  common_label_layers

comp_label_mutsig <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_mutsig, consensus_signature, patient_id), 
       aes(tumor_subsite_rx, label_mutsig, 
           fill = consensus_signature)) + 
  scale_fill_manual(values = clrs$consensus_signature) +
  common_label_layers

patient_label_tbl <- distinct(comp_tbl_sample_sort, patient_id, .keep_all = T)

comp_label_patient_id <- ggplot(patient_label_tbl, aes(tumor_subsite_rx, label_patient_id)) + 
  scale_fill_manual(values = clrs$consensus_signature) +
  geom_text(aes(tumor_subsite_rx, label_patient_id, label = patient_id)) +
  facet_grid(~patient_id, 
             space = "free", scales = "free") +
  coord_cartesian(clip = "off") + 
  theme_void() +
  theme(strip.text = element_blank(),
        strip.background = element_blank(),
        plot.margin = margin(0, 0, 0, 0, "npc"))

hist_plot_wrapper <- function(x = "nrel") {
  if (x == "nrel") {
    xlab <- paste0("Fraction of cells [%]")
    bw <- 5
  }
  if (x == "log10n") {
    xlab <- paste0("Number of cells")
    bw <- 0.2
  }
  p <- ggplot(comp_tbl_sample_sort) +
    ggridges::geom_density_ridges(
      aes_string(x, "cluster_label", fill = "cluster_label"), color = "black",
      stat = "binline", binwidth = bw, scale = 3) +
    facet_grid(label_supersite~., 
               space = "free", scales = "free") +
    scale_fill_manual(values = clrs$cluster_label[[coi]]) + 
    theme(axis.text.y = element_blank(),
          axis.ticks.y = element_blank(),
          axis.title.y = element_blank(),
          axis.line.y = element_blank(),
          strip.text = element_blank(),
          strip.background = element_blank(),
          plot.margin = margin(0, 0, 0, 0, "npc")) +
    scale_x_continuous(expand = c(0, 0)) +
    scale_y_discrete(expand = c(0, 0)) +
    guides(fill = FALSE) +
    labs(x = xlab)
  if (x == "log10n") p <- p + expand_limits(x = c(0, 3)) + 
    scale_x_continuous(expand = c(0, 0), 
                       labels = function(x) parse(text = paste("10^", x)))
  return(p)
}

pcomp1 <- comp_plot_wrapper("n")
pcomp2 <- comp_plot_wrapper("nrel")

pcomp_grid <- plot_grid(comp_label_patient_id, 
                        pcomp1, pcomp2, 
                        comp_label_site, comp_label_rx, comp_label_mutsig,
                        ncol = 1, align = "v", 
                        rel_heights = c(0.15, 0.33, 0.33, 0.06, 0.06, 0.06))

phist1 <- hist_plot_wrapper("log10n")

pcomp_hist_grid <- ggdraw() +
  draw_plot(pcomp_grid, x = 0.01, y = 0, width = 0.85, height = 1) +
  draw_plot(phist1, x = 0.87, y = 0.05, width = 0.12, height = 0.8)

pcomp_hist_grid

# ggsave(paste0("_fig/02_composition_v7_",coi,".pdf"), pcomp_hist_grid, width = 10, height = 2)

3.4.3 site specific cluster enrichment

comp_tbl_z <- comp_tbl_sample_sort %>% 
  filter(therapy == "pre-Rx",
         !(tumor_supersite %in% c("Ascites", "Other"))) %>% 
  group_by(patient_id, cluster_label) %>% 
  arrange(patient_id, cluster_label, nrel) %>% 
  mutate(rank = row_number(nrel),
         z_rank = scales::rescale(rank)) %>% 
  mutate(mean_nrel = mean(nrel, na.rm = T),
         sd_nrel = sd(nrel, na.rm = T),
         z_nrel = (nrel - mean_nrel) / sd_nrel) %>% 
  ungroup()

ggplot(comp_tbl_z) +
  geom_boxplot(aes(tumor_supersite, z_nrel, color = tumor_supersite), 
               outlier.shape = NA) +
  geom_point(aes(tumor_supersite, z_nrel, color = tumor_supersite), position = position_jitter(), size = 0.1) +
  facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
        aspect.ratio = 5) +
  scale_color_manual(values = clrs$tumor_supersite)

ggplot(comp_tbl_z) +
  geom_boxplot(aes(tumor_supersite, z_rank, color = tumor_supersite), 
               outlier.shape = NA) +
  geom_point(aes(tumor_supersite, z_rank, color = tumor_supersite), position = position_jitter(), size = 0.1) +
  facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
        aspect.ratio = 5) +
  scale_color_manual(values = clrs$tumor_supersite)

3.4.4 macrophage diffusion maps

## subset macrophages
seu_obj_mp <- seu_obj_sub %>%
  subset(subset = cluster_label %in% grep("^M1|^M2", unique(seu_obj_sub$cluster_label), value = T)) %>%
  RunUMAP(dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")

write_rds(seu_obj_mp, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/Macrophages_processed.rds")

## subset DCs
seu_obj_dc <- seu_obj_sub %>%
  subset(subset = cluster_label %in% grep("DC|Mast", unique(seu_obj_sub$cluster_label), value = T)) %>%
  RunUMAP(dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")

write_rds(seu_obj_dc, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/DCs_processed.rds")

seu_obj_dc <- read_rds("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/DCs_processed.rds")

seu_obj_mp <- read_rds("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/Macrophages_processed.rds")

root_cell <- "SPECTRUM-OV-031_S1_CD45P_LEFT_FALLOPIAN_TUBE_AACAAGATCATTGCTT"
seu_obj_mp_sub <- subset(seu_obj_mp, cells = c(root_cell, colnames(seu_obj_mp)[colnames(seu_obj_mp)!=root_cell][-1]))

dc_obj <- DiffusionMap(seu_obj_mp_sub@reductions$harmony@cell.embeddings, k = 100)
dc_mat <- dc_obj@eigenvectors
colnames(dc_mat) <- paste0("DC_", 1:ncol(dc_mat))
seu_obj_mp_sub[["DC"]] <- CreateDimReducObject(embeddings = dc_mat, key = "DC_", assay = DefaultAssay(seu_obj_mp_sub))

write_rds(seu_obj_mp_sub, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/Macrophages_processed_filtered.rds")

dpt_obj <- DPT(dc_obj)
write_rds(dpt_obj, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/Macrophages_sub_dpt.rds")
seu_obj_mp_sub$DPT1 <- dpt_obj$DPT1

write_rds(seu_obj_mp_sub, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/Macrophages_processed_filtered.rds")
seu_obj_mp_sub <- read_rds("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/Macrophages_processed_filtered.rds")

# plot_data_sub_sub <- as_tibble(FetchData(seu_obj_sub, c(myfeatures, "cluster_label"))) %>% 
#   left_join(meta_tbl, by = "sample") %>% 
#   mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
#          tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>% 
#   mutate(cell_id = colnames(seu_obj_sub),
#          cluster_label = ordered(cluster_label, levels = my_subtypes),
#          )
#   
# if (cell_sort == "CD45+") {
# plot_data_sub_sub <- filter(plot_data_sub_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
# }
# 
# if (cell_sort == "CD45-") {
# plot_data_sub_sub <- filter(plot_data_sub_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
# }
# 
# ggplot(plot_data_sub_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) + 
#   common_layers_disc +
#   ggtitle("Sub cluster") +
#   #facet_wrap(~cluster_label) +
#   scale_color_manual(values = clrs$cluster_label[[coi]])
# 
# ggplot(plot_data_sub_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) + 
#   common_layers_disc +
#   ggtitle("Patient") +
#   # facet_wrap(~therapy) +
#   scale_color_manual(values = clrs$patient_id_short)
# 
# ggplot(plot_data_sub_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) + 
#   # geom_point(aes(umapharmony_1, umapharmony_2), 
#   #            color = "grey90", size = 0.01, 
#   #            data = select(plot_data_sub_sub, -tumor_supersite)) +
#   common_layers_disc +
#   ggtitle("Site") +
#   # facet_wrap(~therapy) +
#   scale_color_manual(values = clrs$tumor_supersite)
# 
# write_tsv(select(plot_data_sub_sub, cell_id, everything(), -umapharmony_1, -umapharmony_2, -contains("RNA_")), paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_embedding_sub.tsv"))

3.4.5 UMAP & DC

plot_data_mp_sub <- as_tibble(FetchData(seu_obj_mp_sub, c(myfeatures, "cluster_label", "DC_1", "DC_2"))) %>% 
  left_join(meta_tbl, by = "sample") %>% 
  mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
         tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>% 
  mutate(cell_id = colnames(seu_obj_mp_sub),
         cluster_label = ordered(cluster_label, levels = names(clrs$cluster_label[[coi]])))
  
if (cell_sort == "CD45+") {
plot_data_mp_sub <- filter(plot_data_mp_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
}

if (cell_sort == "CD45-") {
plot_data_mp_sub <- filter(plot_data_mp_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
}

ggplot(plot_data_mp_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) + 
  common_layers_disc +
  ggtitle("Sub cluster") +
  #facet_wrap(~cluster_label) +
  scale_color_manual(values = clrs$cluster_label[[coi]])

ggplot(plot_data_mp_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) + 
  common_layers_disc +
  ggtitle("Patient") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$patient_id_short)

ggplot(plot_data_mp_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) + 
  common_layers_disc +
  ggtitle("Site") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$tumor_supersite)

ggplot(plot_data_mp_sub, aes(DC_1, DC_2, color = cluster_label)) + 
  common_layers_disc +
  ggtitle("Sub cluster") +
  #facet_wrap(~cluster_label) +
  scale_color_manual(values = clrs$cluster_label[[coi]])

ggplot(plot_data_mp_sub, aes(DC_1, DC_2, color = patient_id_short)) + 
  common_layers_disc +
  ggtitle("Patient") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$patient_id_short)

ggplot(plot_data_mp_sub, aes(DC_1, DC_2, color = tumor_supersite)) + 
  common_layers_disc +
  ggtitle("Site") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$tumor_supersite)

4 session info

devtools::session_info()

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 3.6.2 (2019-12-12)
##  os       Debian GNU/Linux 10 (buster)
##  system   x86_64, linux-gnu           
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       Etc/UTC                     
##  date     2021-03-06                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package              * version    date       lib
##  abind                  1.4-5      2016-07-21 [2]
##  ape                    5.3        2019-03-17 [2]
##  assertthat             0.2.1      2019-03-21 [2]
##  backports              1.1.10     2020-09-15 [1]
##  bibtex                 0.4.2.2    2020-01-02 [2]
##  Biobase                2.46.0     2019-10-29 [2]
##  BiocGenerics           0.32.0     2019-10-29 [2]
##  BiocParallel           1.20.1     2019-12-21 [2]
##  bitops                 1.0-6      2013-08-17 [2]
##  boot                   1.3-24     2019-12-20 [3]
##  broom                  0.7.2      2020-10-20 [1]
##  callr                  3.4.2      2020-02-12 [1]
##  car                    3.0-8      2020-05-21 [1]
##  carData                3.0-4      2020-05-22 [1]
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## 
## [1] /home/uhlitzf/R/lib
## [2] /usr/local/lib/R/site-library
## [3] /usr/local/lib/R/library

MSK SPECTRUM freeze major subset deep dives

Florian Uhlitz

2021-03-05

1 Cluster markers

1.1 Major Myeloid.super markers for cell assign

1.2 Subtype currated markers

1.3 Subtype cluster markers

2 Clusters

2.1 sizes

2.2 UMAP

3 Filtering out doublet and dissociated clusters

3.1 UMAP

3.2 add module scores and pathway scores

3.3 marker heatmap

3.4 composition

3.4.1 per site

3.4.2 per sample

3.4.3 site specific cluster enrichment

3.4.4 macrophage diffusion maps

3.4.5 UMAP & DC

4 session info